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Sequential Human Activity Recognition Based on Deep Convolutional Network and Extreme Learning Machin 时间:2018-10-05 22:05   来源:未知   作者:admin   点击: 次 Abstract:Human activity recognition (HAR) problems have traditionally been solved by using engineered features obtained by heuristic methods. These methods ignore the time information of the streaming sensor data and cannot achieve sequential human activity recognition. With the use of traditional statistical learning methods, results could easily plunge into the local minimum other than the global optimal and also face the problem of low efficiency. Therefore, we propose a hybrid deep framework based on convolution operations, LSTM recurrent units, and ELM classifier; the advantages are as follows: (1) does not require expert knowledge in extracting features; (2) models temporal dynamics of features; and (3) is more suitable to classify the extracted features and shortens the runtime. All of these unique advantages make it superior to other HAR algorithms. We evaluate our framework on OPPORTUNITY dataset which has been used in OPPORTUNITY challenge. Results show that our proposed method outperforms deep nonrecurrent networks by 6%, outperforming the previous reported best result by 8%. When compared with neural network using BP algorithm, testing time reduced by 38%.
1. Introduction
  Human activity recognition (HAR) is a new technology that can recognize human activities or gestures through computer system. Identified signals can be obtained from different types of detectors, such as audio sensors, image sensors, barometers, and accelerometers. With the rapid development of human-computer interaction (HCI) and wireless body area networks (WBANs), more and more technologies and methods have been applied to the sensor-based human activity recognition. Meanwhile, the growing maturity of ubiquitous computing [1] and machine learning algorithms has made human activity recognition widely used in athletic competition [2], medical care [3], smart home [4], and health care for the old people [5].
  There are two methods of human activity recognition: human activity recognition based on visual images [6, 7] and based on wearable sensors [8]. Human motion analysis in computer vision involves object detection, tracking, and human motion recognition [6]. Computer vision-based human activity recognition method has many limitations. For example, the difficulty of motion detection will be greatly improved under unconstrained conditions, occlusion of the object, and video data acquisition problems for a long time. In addition, the camera needs to be deployed in advance, which cannot be used in some special scenarios, such as emergency rescue. Compared with computer vision, it is more advantageous to obtain signals from wearable sensors than video cameras, due to the following reasons: (1) wearable sensors alleviate the limitations of environmental constraints and fixed scenes that cameras often suffer from [9, 10]; (2) wearable sensors can better protect the privacy of users, as they can acquire signals for a specific target; and (3) multiple sensors can be deployed more accurately and efficiently on the body for signal acquisition.
  In this paper, we study activity recognition based on wearable sensors. This work is motivated by requirements of activity recognition: decreasing dependence on engineered features to address increasingly complex recognition problems, improving recognition accuracy, and improving recognition efficiency. Human activity recognition is challenging due to the large variability of the given action. In order to obtain high accuracy, a large number of data are required. For example, the OPPORTUNITY Activity Recognition Challenge that was organized in 2011, which aims at recognizing activities and gestures in a complex home environment, showed that recognition accuracy of 17 gestures could not exceed 88% [11]. Therefore, addressing the recognition problem in complex scenes will require further improving recognition performance to face a wider set of activities.